Skip to main content
SpringerLink
Log in

Spatial Color Distribution Based Indexing and Retrieval Scheme

  • Conference paper
Man-Machine Interactions

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 59))

Abstract

In this contribution we present a novel approach to the problem of color image indexing and retrieval. The indexing technique is based on the Gaussian Mixture modeling of the histogram of weights provided by the bilateral filtering scheme. In this way the proposed technique considers not only the global distribution of the color pixels comprising the image but also takes into account their spatial arrangement. The model parameters serve as signatures which enable fast and efficient color image retrieval. We show that the proposed approach is robust to color image distortions introduced by lossy compression artifacts and therefore it is well suited for indexing and retrieval of Internet based collections of color images.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data. Journal of the Royal Statistics Society 39, 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  2. Jeong, S., Won, C.S., Gray, R.M.: Image retrieval using color histograms generated by Gauss mixture vector quantization. Computer Vision and Image Understanding 94(1-3), 44–66 (2004)

    Article  Google Scholar 

  3. Kuo, W.J., Chang, R.F.: Approximating the statistical distribution of color histogram for content-based image retrieval. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. 2007–2010 (2000)

    Google Scholar 

  4. Łuszczkiewicz, M., Smołka, B.: Gaussian mixture model based retrieval technique for lossy compressed color images. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 662–673. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  5. Łuszczkiewicz, M., Smołka, B.: A robust indexing and retrieval method for lossy compressed color images. In: Proceedings of the IEEE International Symposium on Image and Signal Processing and Analysis, pp. 304–309 (2007)

    Google Scholar 

  6. Paris, S., Durand, F.: A fast approximation of the bilateral filter using a signal processing approach. International Journal of Computer Vision 39, 1–38 (2007)

    Google Scholar 

  7. Paulus, D., Horecki, K., Wojciechowski, K.: Localization of colored objects. In: Proceedings of International Conference on Image Processing, Vancouver, Canada, pp. 492–495 (2000)

    Google Scholar 

  8. Pioch, N.: Web museum, http://www.ibiblio.org/wm/ (1.03.2008)

  9. Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. In: Proceedings of International Conference on Computer Vision, pp. 59–66 (1998)

    Google Scholar 

  10. Walczak, K.: Image retrieval using spatial color information. In: Skarbek, W. (ed.) CAIP 2001. LNCS, vol. 2124, pp. 53–60. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  11. Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)

    Article  Google Scholar 

  12. Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974)

    Article  MATH  MathSciNet  Google Scholar 

  13. van Berendonck, C., Jacobs, T.: Bubbleworld: A new visual information retreival technique. In: Pattison, T., Thomas, B. (eds.) Proceedings of the Australian Symposium on Information Visualisation, vol. 24, pp. 47–56. ACS, Adelaide (2003)

    Google Scholar 

  14. Bilmes, J.: A gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussian mixture and Hidden Markov Models. Tech. Rep. ICSI-TR-97-021, University of Berkeley (1997)

    Google Scholar 

  15. Hitchcock, F.L.: The distribution of a product from several sources to numerous localities. Journal of Mathematical Physics 23(20), 224–230 (1941)

    MathSciNet  Google Scholar 

  16. Hsu, W., Chua, T.S., Pung, H.K.: An integrated color-spatial approach to content-based image retrieval. In: Proceedings of ACM Multimedia Conference, pp. 305–313 (1995)

    Google Scholar 

  17. Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 762–768 (1997)

    Google Scholar 

  18. Lei, X., Jordan, M.: On convergence properties of the EM algorithm for Gaussian mixtures. Neural Computation 8(1), 129–151 (1996)

    Article  Google Scholar 

  19. Niblack, W., Barber, R.: The QBIC project: Querying images by content, using color, texture, and shape. In: Proceedings of SPIE: Storage and Retrieval for Image and Video Databases, pp. 173–187 (1993)

    Google Scholar 

  20. Pentland, A., Picard, R.W., Sclaroff, S.: Photobook: Tools for content-based manipulation of image databases. In: Proceedings of SPIE: Storage and Retrieval for Image and Video Databases, pp. 34–47 (1994)

    Google Scholar 

  21. Rabiner, L., Juang, B.H.: Fundamentals of Speech Recognition. Prentice Hall PTR, Englewood Cliffs (1993)

    Google Scholar 

  22. Rachev, S.T.: The Monge-Kantorovich mass transference problem and its stochastic applications. Theory of Probability and its Applications 4(XXIX), 647–676 (1984)

    Google Scholar 

  23. Redner, R., Walker, H.: Mixture densities, maximum likelihood and the EM algorithm. SIAM Review 26(2), 195–239 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  24. Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  25. Smith, J., Chang, S.F.: Tools and techniques for color image retrieval. In: Proceedings of SPIE: Storage and Retrieval for Image and Video Databases, pp. 426–437 (1996)

    Google Scholar 

  26. Smołka, B., Szczepański, M., Lukac, R., Venetsanoloulos, A.: Robust color image retrieval for the World Wide Web. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 461–464 (2004)

    Google Scholar 

  27. Swain, M.J., Ballard, D.H.: Color indexing. International Journal on Compututer Vision 7(1), 11–32 (1991)

    Article  Google Scholar 

  28. Wu, J.: On the convergence properties of the EM algorithm. The Annals of Statistics 11(1), 95–103 (1983)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Automatic Control, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Maria Łuszczkiewicz & Bogdan Smołka

Authors
  1. Maria Łuszczkiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bogdan Smołka
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Krzysztof A. Cyran , Stanisław Kozielski , Urszula Stańczyk  & Alicja Wakulicz-Deja , ,  & 

  2. University of Manitoba, Winnipeg, Canada

    James F. Peters

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Łuszczkiewicz, M., Smołka, B. (2009). Spatial Color Distribution Based Indexing and Retrieval Scheme. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds) Man-Machine Interactions. Advances in Intelligent and Soft Computing, vol 59. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00563-3_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-00563-3_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00562-6

  • Online ISBN: 978-3-642-00563-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation